For me, Claude Code was the most impressive innovation this year. Cursor was a good proof of concept but Claude Code is the tool that actually got me to use LLMs for coding.
The kind of code that Claude produces looks almost exactly like the code I would write myself. It's like it's reading my mind. This is a game changer because I can maintain the code that Claude produces.
With Claude Code, there are no surprises. I can pretty much guess what its code will look like 90% to 95% of the time but it writes it a lot faster than I could. This is an amazing innovation.
Gemini is quite impressive as well. Nano banana in particular is very useful for graphic design.
I haven't tried Gemini with coding yet but TBH, Claude Code does such a great job; if I could code any faster, I would get decision fatigue. I don't like rushing into architecture or UX decisions. I like to sit on certain decisions for a day or two before starting implementation. Once you start in a particular direction, it's hard to undo and you may try to double down on the mistake due to sunk cost fallacy. I try hard to avoid that.
I don’t have much time to evaluate tools every months and I have settled on Cursor. I’m curious on what I’m missing when using the same models?
If you switch to Codex you will get a lot of tokens for $200, enough to more consistently use high reasoning as well. Cursor is simply far more expensive so you end up using less or using dumber models.
Claude Code is overrated as it uses many of its features and modalities to compensate for model shortcomings that are not as necessary for steering state of the art models like GPT 5.2
I disagree, the claude models seem the best at tool calling, opus 4.5 seems the smartest, and claude code (+ claude model) seems to make good use of subagents and planning in a way that codex doesn't
I noticed that despite really liking Karpathy and the blog, I was am kind of wincing/involuntarily reacting to the LLM-like "It's not X, its Y"-phrases:
> it's not just a website you go to like Google, it's a little spirit/ghost that "lives" on your computer
> it's not just about the image generation itself, it's about the joint capability coming from text generation
There would be no reaction from me on this 3 years ago, but now this sentence structure is ruined for me
Same, I cringe when I read this structure.
I appreciate Andrej’s optimistic spirit, and I am grateful that he dedicates so much of his time to educating the wider public about AI/LLMs. That said, it would be great to hear his perspective on how 2025 changed the concentration of power in the industry, what’s happening with open-source, local inference, hardware constraints, etc. For example, he characterizes Claude Code as “running on your computer”, but no, it’s just the TUI that runs locally, with inference in the cloud. The reader is left to wonder how that might evolve in 2026 and beyond.
The CC point is more about the data and environmental and general configuration context, not compute and where it happens to run today. The cloud setups are clunky because of context and UIUX user in the loop considerations, not because of compute considerations.
Agree with the GP, though -- you ought to make that clearer. It really reads like you're saying that CC runs locally, which is confusing since you obviously know better.
Yeah, I made some edits to clarify.
What he meant was, agents will probably not be these web abstractions that run in deployed services (langchain, crew); agents meaning the Harnesses (software wrapper) specifically that call the LLM API.
It runs on your computer because of its tooling. It can call Bash. It can literally do anything on the operating system and file system. That's what makes it different. You should think of it like a mech suit. The model is just the brain in a vat connected far away.
One of the most interesting coding agents to run locally is actually OpenAI Codex, since it has the ability to run against their gpt-oss models hosted by Ollama.
codex --oss -m gpt-oss:20b
Or 120b if you can fit the larger model.
What do you find interesting about it, and how does it compare to commercial offerings?
It's rare to find a local model that's capable of running tools in a loop well enough to power a coding agent.
I don't think gpt-oss:20b is strong enough to be honest, but 120b can do an OK job.
Nowhere NEAR as good as the big hosted models though.
Think of it as the early years of UNIX & PC. Running inferences and tools locally and offline opens doors to new industries. We might not even need client/server paradigm locally. LLM is just a probabilistic library we can call.
The section on Claude Code is very ambiguously and confusingly written, I think he meant that the agent runs on your computer (not inference) and that this is in contrast to agents running "on a website" or in the cloud:
> I think OpenAI got this wrong because I think they focused their codex / agent efforts on cloud deployments in containers orchestrated from ChatGPT instead of localhost. [...] CC got this order of precedence correct and packaged it into a beautiful, minimal, compelling CLI form factor that changed what AI looks like - it's not just a website you go to like Google, it's a little spirit/ghost that "lives" on your computer. This is a new, distinct paradigm of interaction with an AI.
However, if so, this is definitely a distinction that needs to be made far more clearly.
Well Microsoft had thier "localhost" AI before CC but that was a ghost without a clear purpose or skill.
> In the same way, LLMs should speak to us in our favored format - in images, infographics, slides, whiteboards, animations/videos, web apps, etc.
You think every Electron app out there re-inventing application UX from scratch is bad, wait until LLMs are generating their own custom UX for every single action for every user for every device. What does command-W do in this app? It's literally impossible to predict, try it and see!
On the other side of the spectrum, I see some of the latest agents, like Codex, take care to get accessibility right -- something not even many humans bother to do.
The distinction Karpathy draws between "growing animals" and "summoning ghosts" via RLVR is the mental model I didn't know I needed to explain the current state of jagged intelligence. It perfectly articulates why trust in benchmarks is collapsing; we aren't creating generally adaptive survivors, but rather over-optimizing specific pockets of the embedding space against verifiable rewards.
I’m also sold on his take on "vibe coding" leading to ephemeral software; the idea of spinning up a custom, one-off tokenizer or app just to debug a single issue, and then deleting it, feels like a real shift.
I've been doing it for months, it's lovely
https://tech.lgbt/@graeme/115749759729642908
It's a stack based on finishing the job Jupyter started. Fences as functions, callable and composable.
Same shape as an MCP. No training required, just walk them through the patterns.
Literally, it's spatially organized. Turns out a woman named Mrs Curwen and I share some thoughts on pedagogy.
There does in fact exist a functor that maps 18th century piano instruction to context engineering. We play with it
LLMs still need to bring clear added value to enterprise and corporate work; otherwise, they remain a geek’s toy.
Big media agencies that claim to use AI rely on strong creative teams who fine-tune prompts and spend weeks doing so. Even then, they don’t fully trust AI to slice long videos into shorter clips for social media.
Heavy administrative functions like HR or Finance still don’t get approval to expose any of their data to LLMs.
What I’m trying to say is that we are still in the early stages of LLM development, and as promising as this looks, it’s still far from delivering the real value that is often claimed.
I would love Andrej's take on the fast models we got this year. Gemini 3 flash and Grok 4 fast have no business being as good + cheap + fast as they are. For Andrej's prediction about LLMs communicating with us via a visual interface we're going to need fast models, but I feel like AI twitter/HN has mostly ignored these.
Do you have a link to anything they wrote about this?
Beyond graduating students, I see model labs as “accelerators/incubators” bundling, launching, and productizing observed ideas that gain traction. The sheer strength of their platforms, the number of eyes watching them, near-zero marginal costs, and seemingly unlimited budgets mean that only slow decision-making can prevent them from becoming the next Amazons of everything.
Notable omission: 2025 is also when the ghosts started haunting the training data. Half of X replies are now LLMs responding to LLMs. The call is coming from inside the dataset.
Any tips to spot this? I want to avoid arguing with a X bot.
Really easy: don't argue on the internet. The approach has many benefits.
also, please just do not use X
> In this world view, nano banana is a first early hint of what that might look like.
What is he referring to here? Is nano banana not just an image gen model? Is it because it's an LLM-based one, and not diffusion?
What's interesting about Nano Banana (and even more so video models like Veo 3) is that they act as a weird kind of world model when you consider that they accept images as input and return images as output.
Give it an image of a maze, it can output that same image with the maze completed (maybe).
There's a fantastic article about that for image-to-video models here: https://video-zero-shot.github.io/
> We demonstrate that Veo 3 can zero-shot solve a broad variety of tasks it wasn't explicitly trained for: segmenting objects, detecting edges, editing images, understanding physical properties, recognizing object affordances, simulating tool use, and much more.
I think he is referring to capability, not architecture, and say that NB is at the point that it is suggestive of the near-future capability of using GenAI models to create their own UI as needed.
NB (Gemini 2.5 Flash Image) isn't the first major-vendor LLM-based image gen model, after all; GPT Image 1 was first.
> I like this version of the meme for pointing out that human intelligence is also jagged in its own different way.
The idea of jaggedicity seems useful to advancing epistemology. If we could identify the domains that have useful data that we fail to extract, we could fill those holes and eventually become a general intelligence ourselves. The task may be as hard as making a list of your blind spots. But now we have an alien intelligence with an outside perspective. While making AI less jagged it might return the favor.
If we keep inventing different kinds of intelligence the sum of the splats may eventually become well rounded.
I don't think it will become well rounded because that is not cost sensitive. Intelligence is sensitive to cost, it is the core constraint shaping it. Any action has a cost - energy, materials, time, opportunity or social. Intelligence is solving the cost equation, if we can't solve it we die. Cost is also why we specialize, in a group we can offload some intelligence to others. LLMs also have their own costs, and are shaped by it into some kind of jagged intelligence, they are no spherical cows either.
I think one of the things that is missing from this post is engaging a bit in trying to answer: what are the highest priority AI-related problems that the industry should seek to tackle?
Karpathy hints at one major capability unlock being UI generation, so instead of interacting with text the AI can present different interfaces depending on the kind of problem. That seems like a severely underexplored problem domain so far. Who are the key figures innovating in this space so far?
In the most recent Demis interview, he suggests that one of the key problems that must be solved is online / continuous learning.
Aside from that, another major issues is probably reducing hallucinations and increasing reliability. Ideally you should be able to deploy an LLM to work on a problem domain, and if it encounters an unexpected scenario it reaches out to you in order to figure out what to do. But for standard problems it should function reliably 100% of the time.
tl;dr seems like llms are maturing on the product side and for day-day usage
Vibe coding is sufficient for job hoppers who never finish anything and leave when the last 20% have to be figured out. Much easier to promote oneself as an expert and leave the hard parts to other people.
All software is not meant to be open-source, in production and working on 100 platforms.
Sometimes the point of the software is to make an app with 2 buttons for your mom to help her do her grocery shopping easier
I’ve found incredible productivity gains writing (vibe coding) tools for myself that will never need to be “productionised” or even used by another person. Heck even I will probably never use the latest log retrieval tool, which exists purely for Claude code to invoke it. There is a ton of useful software yet to be written for which there _is_ no “last 20%”.
These tools are so useful and make you so much more "productive" that you don't think anyone else would want to pay anything for them huh? Did your boss at least give you a big raise for your "productivity" increase, or maybe lay off some of your underperforming coworkers bc you are just so much better now?
Do you mean vibe coding as-in producing unreviewed code with LLMs and prompting at it until it appears to work, or vibe coding as a catch-all for any time someone uses AI-assistance to help them write code?